Automating Prior Authorization Decisions Using Machine Learning and Health Claim Data
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I3P104Keywords:
Prior Authorization, Machine Learning, Healthcare Automation, Health Claim Data, AI In Healthcare, Predictive Modeling, Insurance Claims, Decision Support SystemsAbstract
Although prior authorization (PA) is a necessary process in healthcare that requires doctors to acquire clearance from insurers ahead of starting their certain treatments or medications, it is nonetheless often cumbersome. This approach seeks to control expenses & provide their suitable treatment; yet, sometimes it causes administrative problems for doctors & also patients as well as delays. Reducing inefficiencies & speeding their approvals, machine learning (ML) has emerged as a reasonable substitute for public administration decisions. By use of huge health claim data, ML techniques may spot patterns, project approval outcomes & assist in standardizing & accelerating decision-making processes for insurers. Training predictive models able to differentiate between high- and low-risk events depends critically on health claim data, including a thorough history of patient diagnosis, treatments, and past approvals. Automating typical approvals allows machine learning-driven systems to focus human review on complex situations that really call for professional opinion. Based on preliminary studies & pragmatic implementations, ML-based process automation might significantly reduce processing times, administrative load & improve patient access to necessary treatments. Still, issues such as model transparency, data privacy & their regulatory compliance have to be carefully handled if we are to ensure fairness & their credibility. Incorporating ML into previous authorization processes might help to create a more patient-centered, efficient strategy as healthcare uses digital transformation more & more. Beyond just operational effectiveness, accelerated approvals might improve health outcomes by ensuring fast access to therapy. While human monitoring is important, ML may improve decision-making by optimizing speed, accuracy & equity. Research and industry implementation will be vital for improving these models & solving ethical issues to fully realize the promise of AI-driven prior permission
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